|
Size: 1232
Comment:
|
Size: 1234
Comment:
|
| Deletions are marked like this. | Additions are marked like this. |
| Line 11: | Line 11: |
| * Field (2005)(p.222-223) suggests evaluating a correlation based upon output from a logistic regression. This is based upon the Wald statistic [:FAQ/infmles:which can give misleading] results. | * Field (2005)(pp. 222-223) suggests evaluating a correlation based upon output from a logistic regression. This is based upon the Wald statistic [:FAQ/infmles:which can give misleading] results. |
Computing effect sizes
Cohen's d which is used for t-tests may be computed [http://web.uccs.edu/lbecker/Psy590/escalc3.htm#means%20and%20standard%20deviations with a calculator] or using free [http://www.swin.edu.au/victims/resources/software/effectsize/effect_size_generator.html PC downloadable software.]
SPSS computes partial eta-squared, $$\mbox{Partial } \eta^text{2}$$, on request using ANOVAs. If using General Linear Model>univariate or General Linear Model>Repeated Measures click options and select Estimates of Effect Size. An extra column in the outputted anova tables is produced showing partial eta-squareds of terms in the anova table.
- [:FAQ/power/prop1sn:An EXCEL spreadsheet calculator] computes the one sample chi-square effect size measure, $$\omega$$.
- The Pearson correlation is, itself, an effect size.
- Field (2005)(pp. 222-223) suggests evaluating a correlation based upon output from a logistic regression. This is based upon the Wald statistic [:FAQ/infmles:which can give misleading] results.
You might find [:FAQ/effectSize:A guide to magnitudes of effect sizes] useful.
Reference
Field A (2005) Discovering statistics using SPSS Sage:London.